



from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__UpperCAmelCase			  =			logging.get_logger(__name__)  # pylint: disable=invalid-name



class 							lowerCAmelCase_						(    a__			,       a__				):
       @register_to_config
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None			)	->						Union[str, Any]:
          super().__init__()

          UpperCamelCase       :    int		      =      learnable

          if self.learnable:
             assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
             assert length is not None, "learnable=True requires `length` to be set"

             UpperCamelCase       :    Dict		      =      torch.zeros(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          else:
             UpperCamelCase       :    Optional[Any]		      =      None

          UpperCamelCase       :    Any		      =      torch.nn.Parameter(SCREAMING_SNAKE_CASE_			)






class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   VQModel
       UpperCAmelCase__							:   CLIPTextModel
       UpperCAmelCase__							:   CLIPTokenizer
       UpperCAmelCase__							:   TransformeraDModel
       UpperCAmelCase__							:   LearnedClassifierFreeSamplingEmbeddings
       UpperCAmelCase__							:   VQDiffusionScheduler
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     )	->						Dict:
          super().__init__()

          self.register_modules(
              vqvae=SCREAMING_SNAKE_CASE_,     transformer=SCREAMING_SNAKE_CASE_,     text_encoder=SCREAMING_SNAKE_CASE_,     tokenizer=SCREAMING_SNAKE_CASE_,     scheduler=SCREAMING_SNAKE_CASE_,     learned_classifier_free_sampling_embeddings=SCREAMING_SNAKE_CASE_,     )
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          UpperCamelCase       :    Dict		      =      len(SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) else 1

          # get prompt text embeddings
          UpperCamelCase       :    Union[str, Any]		      =      self.tokenizer(
              SCREAMING_SNAKE_CASE_,     padding='max_length',     max_length=self.tokenizer.model_max_length,     return_tensors='pt',     )
          UpperCamelCase       :    List[str]		      =      text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             UpperCamelCase       :    Optional[int]		      =      self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]			)
             logger.warning(
                 'The following part of your input was truncated because CLIP can only handle sequences up to'
                 F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""			)
             UpperCamelCase       :    Union[str, Any]		      =      text_input_ids[:, : self.tokenizer.model_max_length]
          UpperCamelCase       :    Dict		      =      self.text_encoder(text_input_ids.to(self.device			)			)[0]

          # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
          # While CLIP does normalize the pooled output of the text transformer when combining
          # the image and text embeddings, CLIP does not directly normalize the last hidden state.
          #
          # CLIP normalizing the pooled output.
          # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
          UpperCamelCase       :    Union[str, Any]		      =      prompt_embeds / prompt_embeds.norm(dim=-1,     keepdim=SCREAMING_SNAKE_CASE_			)

          # duplicate text embeddings for each generation per prompt
          UpperCamelCase       :    str		      =      prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_,     dim=0			)

          if do_classifier_free_guidance:
             if self.learned_classifier_free_sampling_embeddings.learnable:
                UpperCamelCase       :    Dict		      =      self.learned_classifier_free_sampling_embeddings.embeddings
                UpperCamelCase       :    List[str]		      =      negative_prompt_embeds.unsqueeze(0			).repeat(SCREAMING_SNAKE_CASE_,     1,     1			)
             else:
                UpperCamelCase       :    Optional[Any]		      =      [''] * batch_size

                UpperCamelCase       :    Tuple		      =      text_input_ids.shape[-1]
                UpperCamelCase       :    Union[str, Any]		      =      self.tokenizer(
                    SCREAMING_SNAKE_CASE_,     padding='max_length',     max_length=SCREAMING_SNAKE_CASE_,     truncation=SCREAMING_SNAKE_CASE_,     return_tensors='pt',     )
                UpperCamelCase       :    Dict		      =      self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]
                # See comment for normalizing text embeddings
                UpperCamelCase       :    Tuple		      =      negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1,     keepdim=SCREAMING_SNAKE_CASE_			)

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             UpperCamelCase       :    str		      =      negative_prompt_embeds.shape[1]
             UpperCamelCase       :    List[Any]		      =      negative_prompt_embeds.repeat(1,     SCREAMING_SNAKE_CASE_,     1			)
             UpperCamelCase       :    int		      =      negative_prompt_embeds.view(batch_size * num_images_per_prompt,     SCREAMING_SNAKE_CASE_,     -1			)

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             UpperCamelCase       :    int		      =      torch.cat([negative_prompt_embeds, prompt_embeds]			)

          return prompt_embeds
       @torch.no_grad()
       def __call__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = 100,     SCREAMING_SNAKE_CASE_ = 5.0,     SCREAMING_SNAKE_CASE_ = 1.0,     SCREAMING_SNAKE_CASE_ = 1,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = "pil",     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = 1,     )	->						Union[ImagePipelineOutput, Tuple]:
          if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    List[str]		      =      1
          elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Optional[int]		      =      len(SCREAMING_SNAKE_CASE_			)
          else:
             raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_			)}"""			)

          UpperCamelCase       :    Optional[Any]		      =      batch_size * num_images_per_prompt

          UpperCamelCase       :    Any		      =      guidance_scale > 1.0

          UpperCamelCase       :    Tuple		      =      self._encode_prompt(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

          if (callback_steps is None) or (
              callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) or callback_steps <= 0)
          ):
             raise ValueError(
                 F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                 F""" {type(SCREAMING_SNAKE_CASE_			)}."""			)

          # get the initial completely masked latents unless the user supplied it

          UpperCamelCase       :    Any		      =      (batch_size, self.transformer.num_latent_pixels)
          if latents is None:
             UpperCamelCase       :    str		      =      self.transformer.num_vector_embeds - 1
             UpperCamelCase       :    Dict		      =      torch.full(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			).to(self.device			)
          else:
             if latents.shape != latents_shape:
                raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""			)
             if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                raise ValueError(
                    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
                    F""" {self.transformer.num_vector_embeds - 1} (inclusive)."""			)
             UpperCamelCase       :    int		      =      latents.to(self.device			)

          # set timesteps
          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_,     device=self.device			)

          UpperCamelCase       :    Any		      =      self.scheduler.timesteps.to(self.device			)

          UpperCamelCase       :    List[Any]		      =      latents

          for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_			)			):
             # expand the sample if we are doing classifier free guidance
             UpperCamelCase       :    List[Any]		      =      torch.cat([sample] * 2			) if do_classifier_free_guidance else sample

             # predict the un-noised image
             # model_output == `log_p_x_0`
             UpperCamelCase       :    List[str]		      =      self.transformer(SCREAMING_SNAKE_CASE_,     encoder_hidden_states=SCREAMING_SNAKE_CASE_,     timestep=SCREAMING_SNAKE_CASE_			).sample

             if do_classifier_free_guidance:
                UpperCamelCase      ,	UpperCamelCase       :    Optional[Any]		      =      model_output.chunk(2			)
                UpperCamelCase       :    Any		      =      model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                model_output -= torch.logsumexp(SCREAMING_SNAKE_CASE_,     dim=1,     keepdim=SCREAMING_SNAKE_CASE_			)

             UpperCamelCase       :    Tuple		      =      self.truncate(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

             # remove `log(0)`'s (`-inf`s)
             UpperCamelCase       :    str		      =      model_output.clamp(-70			)

             # compute the previous noisy sample x_t -> x_t-1
             UpperCamelCase       :    Optional[Any]		      =      self.scheduler.step(SCREAMING_SNAKE_CASE_,     timestep=SCREAMING_SNAKE_CASE_,     sample=SCREAMING_SNAKE_CASE_,     generator=SCREAMING_SNAKE_CASE_			).prev_sample

             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
                callback(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Dict		      =      self.vqvae.config.vq_embed_dim
          UpperCamelCase       :    str		      =      (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
          UpperCamelCase       :    Tuple		      =      self.vqvae.quantize.get_codebook_entry(SCREAMING_SNAKE_CASE_,     shape=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Dict		      =      self.vqvae.decode(SCREAMING_SNAKE_CASE_,     force_not_quantize=SCREAMING_SNAKE_CASE_			).sample

          UpperCamelCase       :    int		      =      (image / 2 + 0.5).clamp(0,     1			)
          UpperCamelCase       :    Dict		      =      image.cpu().permute(0,     2,     3,     1			).numpy()

          if output_type == "pil":
             UpperCamelCase       :    Any		      =      self.numpy_to_pil(SCREAMING_SNAKE_CASE_			)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						torch.FloatTensor:
          UpperCamelCase      ,	UpperCamelCase       :    Optional[int]		      =      torch.sort(SCREAMING_SNAKE_CASE_,     1,     descending=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    str		      =      torch.exp(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Union[str, Any]		      =      sorted_p_x_0.cumsum(dim=1			) < truncation_rate

          # Ensure that at least the largest probability is not zeroed out
          UpperCamelCase       :    Union[str, Any]		      =      torch.full_like(keep_mask[:, 0:1, :],     SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Any		      =      torch.cat((all_true, keep_mask),     dim=1			)
          UpperCamelCase       :    str		      =      keep_mask[:, :-1, :]

          UpperCamelCase       :    List[str]		      =      keep_mask.gather(1,     indices.argsort(1			)			)

          UpperCamelCase       :    str		      =      log_p_x_0.clone()

          UpperCamelCase       :    Dict		      =      -torch.inf  # -inf = log(0)

          return rv










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  ["RoFormerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RoFormerForCausalLM",
           "RoFormerForMaskedLM",
           "RoFormerForMultipleChoice",
           "RoFormerForQuestionAnswering",
           "RoFormerForSequenceClassification",
           "RoFormerForTokenClassification",
           "RoFormerLayer",
           "RoFormerModel",
           "RoFormerPreTrainedModel",
           "load_tf_weights_in_roformer",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRoFormerForCausalLM",
           "TFRoFormerForMaskedLM",
           "TFRoFormerForMultipleChoice",
           "TFRoFormerForQuestionAnswering",
           "TFRoFormerForSequenceClassification",
           "TFRoFormerForTokenClassification",
           "TFRoFormerLayer",
           "TFRoFormerModel",
           "TFRoFormerPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FlaxRoFormerForMaskedLM",
           "FlaxRoFormerForMultipleChoice",
           "FlaxRoFormerForQuestionAnswering",
           "FlaxRoFormerForSequenceClassification",
           "FlaxRoFormerForTokenClassification",
           "FlaxRoFormerModel",
           "FlaxRoFormerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






